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AI technique for load flow planning and contingency analysis
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A Survey of Data Clustering Techniques
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Design and Analysis of Buffer-Aided Cooperative Networks using Deterministic and Reinforcement Learning Techniques
Published 2022“…Next, for more complex setups, deterministic analysis becomes cumbersome, thus reinforcement learning techniques are used to efficiently boost the performance. …”
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MSLP: mRNA subcellular localization predictor based on machine learning techniques
Published 2023“…<h3>Background</h3><p dir="ltr">Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. …”
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Untrained Neural Network Priors for Inverse Imaging Problems: A Survey
Published 2022“…In this paper, we present a comprehensive review of such studies and various UNNP applications for different tasks and highlight various open research problems which require further research.…”
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Artificial Intelligence Frameworks for Sentiment Variations’ Reasoning and Emerging Topic Detection
Published 2021“…Utilizing Sentiment Analysis techniques to monitor public opinions on social media has been an essential yet challenging task in the field of Artificial Intelligence (AI). …”
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Low-Light Image Enhancement for Object Classification using Deep Learning
Published 2021“…Low-light image (LLI) enhancement is an important image processing task that aims at improving the illumination of images taken under low-light conditions. …”
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Developing a framework for network security behavior integrated with the organization data management system to predict the threats
Published 2022“…Network security management becomes an essential task in all organizations to protect their information and communication. …”
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A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture
Published 2024“…Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. …”
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